Vehicular Roadway Hazard Detection With Early Avoidance Alerts
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Solution Overview
Problem
Current vehicular systems and methods fail to effectively detect and communicate real-time roadway hazards to vehicles and authorities, leading to increased vehicular damage, injuries, and deaths due to delayed remediation of hazards like potholes and other obstacles.
Innovation Solution
Implementing a machine-learning based roadway hazard detection system that uses various sensors and pattern recognition to identify hazards, transmit data to other vehicles and authorities, and trigger remedial actions, such as altering driving paths or notifying maintenance entities, to facilitate timely hazard remediation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are used to detect roadway hazards, then detection capability is improved, but response time is insufficient due to vehicle position limitations
Solution Approach 1:
The system performs preliminary detection of roadway hazards using sensors (LiDAR, cameras) before the vehicle reaches the hazard location. By detecting hazards early and calculating avoidance paths in advance, the system enables the vehicle to respond timely by adjusting speed or changing lanes before reaching the dangerous area, thus resolving the contradiction between detection capability and response time.
2Measurement precision
If vehicular sensors recognize upcoming roadway hazards, then hazard detection is improved, but vehicle position prevents course change
Solution Approach 1:
The system dynamically adjusts vehicle control based on real-time hazard detection and vehicle state. When a hazard is detected, the control system calculates optimal avoidance maneuvers (steering angle, speed adjustment) and executes them dynamically, allowing the vehicle to change course even when initially positioned close to the hazard, thus resolving the contradiction between hazard recognition accuracy and vehicle maneuverability.
3Loss of time
If delayed remediation of roadway hazards occurs, then hazard removal time is increased, but vehicular damage and injuries increase
Solution Approach 1:
The system establishes a feedback loop where sensor data about roadway hazards is continuously monitored, analyzed, and used to trigger alerts to drivers and authorities. This real-time feedback enables rapid response to hazards, allowing drivers to avoid them and authorities to prioritize remediation, thus reducing vehicular damage and injuries despite delayed physical hazard removal, resolving the contradiction between remediation time and harmful effects.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables more accurate and rapid detection of hazards, reducing vehicular damage and injuries by enabling real-time notifications and efficient remediation of roadway hazards, thereby improving road safety and maintenance efficiency.
Implementation Method 1
The roadway conditions may be detected using a variety of sensors (e.g., LiDAR) and cameras
Data Source
AI summary
Systems and methods for detecting and remediating roadway hazards are disclosed. A machine learning model is trained on a dataset related to roadway items. Input data is collected by a data collection engine and provided to a pattern recognizer. The pattern recognizer extracts roadway features and recognized patterns from the input data and provide the extracted features to a trained machine learning model. The trained model compares the extracted features to the model, and a risk value is generated. The risk value is compared to a risk value threshold. If the risk value is equal to or exceeds the risk threshold, then the input data may be classified as a roadway hazard. Remedial action is subsequently be triggered.